Road roughness significantly affects vehicle vibrations and ride quality. We introduce a Kalman filter (KF)-based method for estimating road roughness in terms of the international roughness index (IRI) by fusing inertial and speed measurements, offering a cost-effective solution for pavement monitoring. The method involves system identification on a physical vehicle to estimate realistic model parameters, followed by KF-based reconstruction of the longitudinal road profile to compute IRI values. It explores IRI estimation using vertical and lateral vibrations, the latter more common in modern vehicles. Validation on 230 km of real-world data shows promising results, with IRI estimation errors ranging from 1% to 10% of the reference values. However, accuracy deteriorates significantly when using only lateral vibrations, highlighting their limitations. These findings demonstrate the potential of KF-based estimation for efficient road roughness monitoring.

This paper investigates time synchronization in distributed sensor networks using the primary synchronization signal (PSS) in Long-Term Evolution (LTE). Two LTE-based time synchronization methods with receiver-to-receiver characteristics have been evaluated in simulations, the passive Scalable Wireless Network Synchronization (SWINS) and the active Reference Broadcast Synchronization (RBS). In addition, small scale hardware experiments were conducted for SWINS. Time synchronization is crucial for many applications, such as power grid monitoring, communication systems, and sensor data fusion. Global Navigation Satellite Systems (GNSS) are currently the state of the art for time synchronization in distributed wireless sensor networks. However, GNSS is vulnerable to jamming and spoofing, which requires alternative methods, e.g., using signals of opportunity. Both evaluated methods achieve accuracy comparable to GNSS in Matlab simulations with high SNR. SWINS performs better in synchronized LTE networks while RBS is superior in unsynchronized networks, which means that the LTE base station transmissions are not synchronous. The disturbance and sensitivity analysis indicates that joint clock offset and position estimation is preferable to sole clock offset estimation when the receiver position uncertainty exceeds 5 and 7 meters for SWINS and RBS respectively. The hardware experiments, using real experimental data, verify the simulation results by showing promising results and potential for real-world application.

Accurate positioning, navigation and timing (PNT) is important for military and civilian applications alike. In recent years navigation using signals of opportunity (SOPs) has received increased interest as a complement or alternative to Global Navigation Satellite Systems (GNSS). We previously proposed a localization framework using opportunistic digital television signals. In it, a navigator localizes itself aided by a stationary base station at a known location, using two time difference of arrival (TDOA) measurements and one twoway ranging (TWR) measurement. In this work we implement and evaluate the proposed framework using real measurements, and based on this propose an extended model which includes the difference in clock bias between the navigator and the base station. The experimental results show that the framework can achieve a root mean square error (RMSE) in absolute position of less than 50 m provided that at least three TDOA measurements are used (with or without TWR), and sometimes if only two TDOA measurements are used in combination with a TWR measurement. We also show experimentally that the transmitter clocks are stable enough for the navigator to extract the TDOA measurements relying only on its own current measurements and previously collected data by the base station. Thus, by replacing the TWR measurement with a third TDOA measurement, the base station need not be active or communicate with the navigator during the localization. In scenarios where communication is undesirable or impossible, this can be advantageous.

Due to recent events that have demonstrated the vulnerabilities of global navigation satellite systems (GNSS) there has been an increased interest in alternative methods for localization. One traditional alternative method is terrain-aided navigation (TAN), where a platform localizes itself by measuring the terrain elevation and comparing it to a digital elevation map (DEM). While single-agent TAN has been extensively studied, multi-agent TAN remains less explored. This paper addresses the multi-agent TAN problem with a focus on its properties. We formulate a weighted least squares (WLS) estimator for computing a snapshot solution to the problem and formulate a CramérRao Lower Bound (CRLB) to evaluate it. Using the expressions for the estimator and the CRLB we are able to highlight some insightful properties of the problem. The findings are verified in a simulation study where we evaluate the performance with respect to the altitude sensor accuracy, the group formation accuracy, the number of agents and their formation. Notably, we observe that the solution is relatively insensitive to errors in agent position, suggesting that low-accuracy inertial navigation systems and distance sensors are sufficient for determining their positions. Increasing the number of agents beyond a few seems to have a large effect on both the efficiency and robustness of the estimator, which lessens as the number of agents increases. However, increasing the number of agents does not compensate for poor altitude sensor quality. Additionally, while spatial separation between agents is important for effective map utilization, further separation beyond a certain point does not enhance performance. These findings provide design guidelines for multi-agent TAN systems and identify areas for further research.

Passive bistatic radar (PBR) is a cost-effective choice for detection and tracking of aircraft. In this paper we present how the Poisson multi-Bernoulli mixture (PMBM) filter is applied in a multi-target tracking application with multistatic PBR. To handle the PBR measurements, it is proposed that a Gaussian mixture target spatial density is used to represent the target state. A state dependent probability of detection model for PBR is presented and how it is used to design the target birth model. Simulated and experimental data are used to evaluate the performance of the described approaches.

As human settlement expands into the natural habitats of wild animals, the conflicts between humans and wildlife increases. The human-elephant conflict causes a tremendous amount of damage, often to poor villages close to the savannah. In this paper, we continue our earlier reported research on a geophone network aimed for elephant localisation by focusing on the detection challenge. We have now collected larger sets of seismic data with footsteps from both elephants and other big animals including humans. To detect the footsteps, a method is developed that analyses features of the geophone signal, which are then compared to those of an elephant footstep. The method detects $54 \%$ of the footsteps and has a classification accuracy of $89 \%$. Subsequently, the detected elephant footstep is used to calculate the direction of arrival (DOA) angle using a delay-andsum beamformer. The direction to an elephant is estimated with good precision on distances ranging from 8 to 30 meters. This research, not only, showcases a practical solution for mitigating human-elephant conflicts, but also underscores the potential of seismic technology in wildlife management and conservation efforts.

Due to vulnerabilities of Global Navigation Satellite Systems (GNSS) there is an increased interest in alternative navigation solutions, such as using signals of opportunity (SOPs). We propose a system where a mobile navigator localizes itself using two-way ranging (TWR) measurements to a stationary base station at a known location as well as time difference of arrival (TDOA) measurements from two terrestrial digital television transmitters. We investigate the feasibility of such a system by deriving the Cramér-Rao Lower Bound (CRLB) for varying noise levels and optimizing the placement of the base station, using the real-life positions of transmitters in the area around LinkÖping, Sweden. We simulate measurements and compute snapshot estimates, verifying that root mean square errors similar to the CRLB can be obtained. The results indicate that for the investigated levels of TWR noise, as long as the TDOA noise is sufficiently low it could be possible to achieve errors of a few tens of meters.

It is shown how dense optical flow obtained using deep learning can be used to provide high quality visual odometry. The obtained odometric information can be utilized as a component to reduce the inherent drift of inertial navigation systems (INS). This could be a key component to provide autonomous system with robust localization capability in GNSS denied environments. The method leverages the power of estimating optical flow from neural networks, which can provide reliable results even when feature based optical flow fails. Comparison of different methods to decide which points of the dense optical flow that should be used to provide as good visual odometry as possible has been performed. Furthermore, it is exemplified how the methodology can help limit the drift in an INs.

High-definition map with accurate lane-level information is crucial for autonomous driving, but the creation of these maps is a resource-intensive process. To this end, we present a cost-effective solution to create lane-level roadmaps using only the global navigation satellite system (GNSS) and a camera on customer vehicles. Our proposed solution utilizes a prior standard-definition (SD) map, GNSS measurements, visual odometry, and lane marking edge detection points, to simultaneously estimate the vehicle’s 6 D pose, its position within a SD map, and also the 3D geometry of traffic lines. This is achieved using a Bayesian simultaneous localization and multi-object tracking filter, where the estimation of traffic lines is formulated as a multiple extended object tracking problem, solved using a trajectory Poisson multi-Bernoulli mixture (TPMBM) filter. In TPMBM filtering, traffic lines are modeled using B-spline trajectories, and each trajectory is parameterized by a sequence of control points. The proposed solution has been evaluated using experimental data collected by a test vehicle driving on highway. Preliminary results show that the traffic line estimates, overlaid on the satellite image, generally align with the lane markings up to some lateral offsets.

The Particle filter can in theory estimate the state of any nonlinear system, but in practice it suffers from an exponential complexity in terms of the number of particles as the dimension of the state increases. The marginalized particle filter can potentially reduce this problem by improving the estimates, particularly for lower number of particles. However, it turns out that for certain systems, it does not provide any improvement in the accuracy of the estimate. The core cause of degeneracy is linked to when the uncertainty of the linear state conditioned on the nonlinear state is 0. Conditions for determining when this occurs are presented and applied to common constant velocity, constant acceleration and constant jerk models with various sampling methods. Interestingly, some combinations are useful while others should be avoided. These findings are supported using simulated systems.

Network-centric multitarget tracking under communication constraints is considered, where dimension-reduced track estimates are exchanged. Previous work on target tracking in this subfield has focused on fusion aspects only and derived optimal ways of reducing dimensionality based on fusion performance. In this work we propose a novel problem formalization where estimates are reduced based on association performance. The problem is analyzed theoretically and problem properties are derived. The theoretical analysis leads to an optimization strategy that can be used to partly preserve association quality when reducing the dimensionality of communicated estimates. The applicability of the suggested optimization strategy is demonstrated numerically in a multitarget scenario.

A new class of iterated linearization-based nonlinear filters, dubbed dynamically iterated filters, is presented. Contrary to regular iterated filters such as the iterated extended Kalman filter (IEKF), iterated unscented Kalman filter (IUKF) and iterated posterior linearization filter (IPLF), dynamically iterated filters also take nonlinearities in the transition model into account. The general filtering algorithm is shown to essentially be a (locally over one time step) iterated Rauch-Tung-Striebel smoother. Three distinct versions of the dynamically iterated filters are especially investigated: analogues to the IEKF, IUKF and IPLF. The developed algorithms are evaluated on 25 different noise configurations of a tracking problem with a nonlinear transition model and linear measurement model, a scenario where conventional iterated filters are not useful. Even in this “simple” scenario, the dynamically iterated filters are shown to have superior root mean-squared error performance as compared with their respective baselines, the EKF and UKF. Particularly, even though the EKF diverges in 22 out of 25 configurations, the dynamically iterated EKF remains stable in 20 out of 25 scenarios, only diverging under high noise.

Human-wildlife conflicts are a global problem which is central to the Global Goal 15 (life on land). One particular case is elephants, that can cause harm to both people, property and crops. An early warning system that can detect and warn people in time would allow effective mitigation measures. The proposed method is based on a small local network of geophones that sense the seismic waves of elephant footsteps. It is known that elephant footsteps induce low frequency ground waves that can be picked up by geophones in the ground. First, a method is described that detect the particular signature of such footsteps, and then the detections are used to estimate the direction of arrival (DOA). Finally, a Kalman filter is applied to the measurements in order to track the elephant. Field tests performed at a local zoo shows promising results with accurate DOA estimates at 15 meters distance and acceptable accuracy at 40 meters.

We consider a decentralized sensor network of multiple nodes with limited communication capability where the cross-correlations between local estimates are unknown. To reduce the bandwidth the individual nodes determine which subset of local information is the most valuable from a global perspective. Three information selection methods (ISM) are derived. The proposed ISM require no other information than the communicated estimates. The simulation evaluation shows that by using the proposed ISM it is possible to determine which subset of local information is globally most valuable such that both reduced bandwidth and high performance are achieved.

An inference method for Gaussian process augmented state-space models are presented. This class of grey-box models enables domain knowledge to be incorporated in the inference process to guarantee a minimum of performance, still they are flexible enough to permit learning of partially unknown model dynamics and inputs. To facilitate online (recursive) inference of the model a sparse approximation of the Gaussian process based upon inducing points is presented. To illustrate the application of the model and the inference method, an example where it is used to track the position and learn the behavior of a set of cars passing through an intersection, is presented. Compared to the case when only the state-space model is used, the use of the augmented state-space model gives both a reduced estimation error and bias.

Lidar-based positioning in a 2D map is analyzed as a method to provide a robust, high accuracy, and infrastructure-free positioning system required by the automation development in underground mining. Expressions are derived that highlight separate information contributions to the obtained position accuracy. This is used to develop two new methods that efficiently select which subset of available lidar rays to use to reduce the computational complexity and allow for online processing with minimal loss of accuracy. The results are verified in simulations of a mid-articulated underground loader in a mine. The methods are shown to be able to reduce the number of rays needed without considerably affecting the performance, and to be competitive with currently used methods. Furthermore, simulations highlight the effects of errors in the map and other map properties, and how imperfect maps degrades the performance of different selection strategies.

An approach for belief space planning is presented, where knowledge about the landmark density is used as prior, instead of explicit landmark positions. Having detailed maps of landmark positions in a previously unvisited environment is considered unlikely in practice. Instead, it is argued that landmark densities should be used, as they could be estimated from other sources, such as ordinary maps or aerial imagery. It is shown that it is possible to use virtual landmarks to approximate the landmark density to solve the presented problem. This approximation is also shown to give small errors during evaluation. The approach is tested in a simulated environment, in conjunction with an extended information filter (EIF), where the computed path is shown to be superior compared to other alternative paths used as benchmarks.

Modern maritime navigation is heavily dependent on satellite systems. Availability of an accurate position is critical for safe operations, but satellite-based navigation systems are vulnerable to interference, jamming, and spoofing. In this work, we propose a method for maritime navigation independent of GNSS, able to provide absolute positioning of the vessel based on marine radar scans. A measurement model is presented where a Digital Elevation Model is used to predict the output of a marine radar, given a hypothetical position. The model, as used by an on-line particle filter, is used to track the movements of a ship from real recorded data. This demonstrates the feasibility of this method for robust positioning, without the need of external positioning signals, in a maritime environment. The tracking only uses sensors commonly available on maritime vessels, and demonstrates its application using freely available elevation data.

A wearable microphone array platform is used to localize stationary sound sources and amplify the sound in the desired directions using several beamforming methods. The platform is equipped with inertial sensors and a magnetometer allowing predictions of source locations during orientation changes and compensation for the displacement in the array configuration. The platform is modular, open and 3D printed to allow for easy reconfiguration of the array and for reuse in other applications, e.g., mobile robotics. The software components are based on open source. A new method for source localization and signal reconstruction using Taylor expansion of the signals is proposed. This and various standard and non-standard Direction of Arrival (DOA) methods are evaluated in simulation and experiments with the platform to track and reconstruct multiple and single sources. Results show that sound sources can be localized and tracked robustly and accurately while rotating the platform and that the proposed method outperforms standard methods at reconstructing the signals.

The number of sensors used in tracking scenarios is constantly increasing, this puts high demands on the tracking methods to handle these data streams. Central processing (ideally optimal) puts high demands on the central node, is sensitive to inaccurate sensor parameters, and suffers from the single point of failure problem. Decentralizing the tracking can improve this, but may give considerable performance loss. The newly presented inverse covariance intersection method, proven to be consistent, even under unknown track cross-correlations, is benchmarked against alternatives. Different track-to-track methods, including smoothed association over a window, are compared. A scenario with objects tracked in multiple cameras, not necessarily optimized for tracking, are used to give realism to the evaluations.

A model-based method to perform odometry using an array of magnetometers that sense variations in a local magnetic field is presented. The method requires no prior knowledge of the magnetic field, nor does it compile any map of it. Assuming that the local variations in the magnetic field can be described by a curl and divergence free polynomial model, a maximum likelihood estimator is derived. To gain insight into the array design criteria and the achievable estimation performance, the identifiability conditions of the estimation problem are analyzed and the Cramér-Rao bound for the one-dimensional case is derived. The analysis shows that with a second-order model it is sufficient to have six magnetometer triads in a plane to obtain local identifiability. Further, the Cramér-Rao bound shows that the estimation error is inversely proportional to the ratio between the rate of change of the magnetic field and the noise variance, as well as the length scale of the array. The performance of the proposed estimator is evaluated using real-world data. The results show that, when there are sufficient variations in the magnetic field, the estimation error is of the order of a few percent of the displacement. The method also outperforms current state-of-the-art method for magnetic odometry.

Acoustic frequency tracking of a harmonic signal with continuously varying frequency is considered. The Rao-Blackwellized point mass filter (RBPMF), previously proposed by the authors for mechanical vibration tracking, is applied to the problem. The RBPMF is compared with two periodogram-based methods, and the similarities and differences between them are explained. Both experimental and simulation results in a Doppler frequency tracking scenario are presented, and the results show that the RBPMF can have significantly less estimation error than the competing methods.

In polar region operations, drift ice positioning and tracking is useful for both scientific and safety reasons. At its core is a Multi-Target Tracking (MTT) problem in which currents and winds make motion modeling difficult. One recent algorithm in the MTT field, employed in this paper, is the Labeled Multi-Bernoulli (LMB) filter. In particular, a proposed reformulation of the LMB equations exposes a structure which is exploited to propose a compact algorithm for the generation of the filter's posterior distribution. Further, spatial indexing is applied to the clustering process of the filter, allowing efficient separation of the filter into smaller, independent parts with lesser total complexity than that of an unclustered filter. Many types of sensors can be employed to generate detections of sea ice, and in this paper a recorded dataset from a Terrestrial Radar Interferometer (TRI) is used to demonstrate the application of the Spatially Indexed Labeled Multi-Bernoulli filter to estimate the currents of an observed area in Kongsfjorden, Svalbard.